
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "191"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 101 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 97 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.387275	-0.273361	6.493748	-0.974470	1.437577	-0.013585	4.682075	0.327724	0.7218	0.7540	0.3352	11.618299	12.543577
2459593	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.854148	1.315898	8.331778	-0.662818	2.804223	1.094116	2.607958	0.757392	0.6542	0.6593	0.3246	4.586695	4.473735
2459592	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	0.830534	-0.496939	1.461232	-0.604639	3.024418	0.847337	1.555600	2.081451	0.0320	0.0332	0.0022	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.464349	1.739033	9.566233	-0.621599	2.417048	2.090784	-0.734069	9.596871	0.6210	0.6402	0.4150	3.434109	3.349586
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.669834	1.502939	8.535678	-0.606800	2.194399	-0.668654	-0.299426	0.238677	0.6180	0.6312	0.4144	4.518987	4.622157
2459589	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.399432	1.522697	7.036597	-0.798545	2.185552	-0.816723	3.310872	-0.229550	0.6545	0.6575	0.3380	5.143005	5.156630
2459588	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.289203	1.533388	10.147308	-0.323174	1.945679	-0.217889	3.111143	0.100978	0.6789	0.6703	0.3087	4.919336	4.567010
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	2.354611	1.691987	14.560952	0.277090	7.547118	2.685409	0.563179	1.660573	nan	nan	nan	4.162356	3.960378
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	15.14%	1.130199	0.084682	0.106834	-0.870815	-0.103596	0.558966	0.999281	0.929114	0.6234	0.6383	0.3928	1.397095	1.384905
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.354633	-1.070749	-0.017982	-0.000054	10.466836	1.598934	2.082043	1.278220	0.0324	0.0329	0.0012	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	17.846044	17.612179	25.640962	6.950416	28.869818	11.378965	-0.282119	0.348385	0.5802	0.6039	0.4147	6.166574	5.448977
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459582	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.762625	0.933761	8.891321	2.131607	9.830988	-0.145892	-1.323984	0.360902	0.6180	0.6432	0.4323	3.976409	4.101217
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.337745	1.652142	9.097278	1.374261	7.621562	11.214250	-0.809089	1.990151	0.6176	0.6439	0.4338	3.598289	3.437894
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.242415	0.769624	10.174356	0.767626	8.819475	-1.049861	-1.502077	-0.501845	0.6226	0.6487	0.4271	5.448515	5.604882
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.061595	0.950469	11.786673	2.314544	10.404886	0.841069	-2.013626	12.538906	0.6164	0.6419	0.4307	4.296337	4.438519
2459578	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	3.921039	1.205395	5.418323	0.484982	4.352639	1.929914	1.812305	12.748877	0.0310	0.0370	0.0011	nan	nan
2459577	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.104767	1.945655	8.696681	2.409130	9.239870	3.268490	-0.173045	5.560627	0.6182	0.6466	0.4286	4.203630	4.639949
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459575	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.022483	1.309511	6.979713	1.652682	6.819240	-0.318099	5.259103	1.624593	0.6998	0.7242	0.3873	4.481305	4.828166
2459574	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.980783	0.953749	6.364365	1.493550	9.114019	0.218306	0.323087	1.325913	0.6324	0.6571	0.4371	3.669061	3.741343
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.651747	1.303321	12.687380	3.279507	10.905478	2.289645	-1.330946	15.667583	0.6370	0.6630	0.4330	7.138553	6.354322
2459572	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.240069	0.867034	6.596925	1.719988	7.114368	-0.199647	-2.208841	1.142490	0.6425	0.6685	0.4317	4.294150	4.456767
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	3.708893	1.430625	5.635541	0.941203	2.842584	0.415003	2.022145	-1.116538	0.0339	0.0347	0.0010	nan	nan
2459570	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.053532	5.707229	-0.398458	4.718051	0.159015	4.800780	0.391388	3.502100	0.6320	0.6715	0.4168	0.000000	0.000000
2459569	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.781513	1.289970	7.632113	2.110211	6.460634	1.498869	5.266842	1.437550	0.7172	0.7440	0.2785	7.400638	8.257748
2459566	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.005153	0.844727	9.001989	2.455659	8.203654	0.210985	1.359004	2.938093	0.6395	0.6641	0.4434	0.000000	0.000000
2459565	digital_ok	0.00%	-	-	-	93.33%	0.00%	1.074879	1.074879	0.972728	0.972728	0.664032	0.664032	0.146470	0.146470	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.000121	0.598485	-4.387892	-1.243167	11.598663	4.295821	-0.020180	1.223992	0.6618	0.6742	0.4391	3.809952	3.426215
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.149141	1.163686	6.127927	-0.662326	-0.228659	-0.365749	0.684172	2.518014	0.6587	0.6702	0.4352	3.227077	3.010954
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.406298	-0.510984	-0.161727	-2.027940	-1.710103	-0.712111	-0.547316	0.585823	0.6514	0.6777	0.4331	1.752634	1.623948
2459561	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.972861	0.103529	0.343960	-1.918752	-1.311068	1.097346	-1.679391	10.670543	0.6525	0.6796	0.4348	4.547575	4.498809
2459560	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.554540	-0.186688	-0.020815	-2.003251	-1.247365	-0.012870	-1.072903	8.359841	0.6524	0.6764	0.4417	3.678523	3.490841
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	1.08%	1.108029	-0.666106	0.523312	-2.293110	-0.870609	-0.611317	-0.998222	1.271677	0.6526	0.6781	0.4245	1.629677	1.513509
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.758015	0.922852	10.386146	2.561713	9.342666	-0.039204	-1.062110	9.145620	0.6582	0.6787	0.4313	6.789093	6.932946
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0302	0.0322	0.0003	nan	nan
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.153225	0.924343	10.845794	2.737122	8.616592	1.432346	-3.121862	11.554145	0.6496	0.6689	0.4373	6.384129	6.481314
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6563	0.6807	0.4416	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6552	0.6789	0.4385	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.075538	1.951888	14.168612	5.014818	12.909825	0.395158	-0.208119	-0.637512	0.6549	0.6785	0.4414	0.000000	0.000000
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.786130	-0.311431	0.242567	-2.180858	-1.765600	-0.198418	-1.550585	3.792714	0.6538	0.6807	0.4351	1.752861	1.558001
2459550	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0306	0.0326	0.0007	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.590062	1.092547	10.382897	2.307114	9.243759	-0.156796	-1.355689	6.866917	0.6535	0.6797	0.4460	3.518229	3.432744
2459542	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.111139	1.103008	12.555060	1.728448	4.259360	2.425969	-1.411044	-0.736799	0.7321	0.7401	0.3852	4.526534	4.786280
2459541	digital_ok	100.00%	1.34%	1.34%	0.00%	100.00%	0.00%	4.531962	1.701424	12.598865	4.032117	7.682845	3.290861	3.258159	1.135555	0.7092	0.7239	0.3785	0.000000	0.000000
2459540	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.027470	0.833648	1.623121	-0.994987	-1.170360	11.795658	-1.149933	2.099609	0.7251	0.7351	0.3773	4.057945	4.053236
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.868804	2.094032	16.945580	4.413713	14.578363	24.747477	-3.298388	2.239899	0.6702	0.6956	0.4674	0.000000	0.000000
2459535	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.733914	0.955860	1.190986	-0.652093	-2.585664	-0.627417	-0.600680	6.212336	0.7495	0.7509	0.3985	1.064686	1.073884
2459534	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.873055	0.957102	0.228854	-0.640235	-2.010366	-0.055052	-1.051124	6.645235	0.7422	0.7388	0.3902	0.848375	0.851177
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.311021	2.538680	13.805592	0.988116	9.761046	0.711726	-0.752117	7.852258	0.6666	0.6936	0.4470	4.472957	5.746122
2459530	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.777293	0.494812	0.200411	-1.569574	-1.918007	-0.016261	-1.414553	4.798589	0.6834	0.7063	0.4452	0.834335	0.841110
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	4.348890	1.461967	4.790532	0.231923	4.328226	-1.010346	0.373193	-0.914102	0.0371	0.0402	0.0010	nan	nan
2459508	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.250994	2.404036	8.968549	1.922187	9.203696	1.086085	8.456974	2.953066	0.8873	0.9499	0.2260	0.000000	0.000000
2459505	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.387029	2.305419	11.591093	2.663150	2.898984	0.781411	-0.927089	-0.879126	0.8772	0.8527	0.2145	12.853536	13.456710
2459503	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.456632	3.599355	11.655989	3.609219	19.607102	5.856642	1.266494	-1.028301	0.1130	0.1064	0.0261	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 191 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 191, Node 15:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 191: 2459625

Antenna 191: 2459624

Antenna 191: 2459623

Antenna 191: 2459622

Antenna 191: 2459621

Antenna 191: 2459620

Antenna 191: 2459619

Antenna 191: 2459618

Antenna 191: 2459617

Antenna 191: 2459616

Antenna 191: 2459615

Antenna 191: 2459614

Antenna 191: 2459613

Antenna 191: 2459612

Antenna 191: 2459611

Antenna 191: 2459610

Antenna 191: 2459609

Antenna 191: 2459608

Antenna 191: 2459607

Antenna 191: 2459605

Antenna 191: 2459604

Antenna 191: 2459603

Antenna 191: 2459602

Antenna 191: 2459598

Antenna 191: 2459597

Antenna 191: 2459596

Antenna 191: 2459595

Antenna 191: 2459594

Antenna 191: 2459593

Antenna 191: 2459592

Antenna 191: 2459591

Antenna 191: 2459590

Antenna 191: 2459589

Antenna 191: 2459588

Antenna 191: 2459587

Antenna 191: 2459586

Antenna 191: 2459585

Antenna 191: 2459584

Antenna 191: 2459583

Antenna 191: 2459582

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Antenna 191: 2459580

Antenna 191: 2459579

Antenna 191: 2459578

Antenna 191: 2459577

Antenna 191: 2459576

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Antenna 191: 2459573

Antenna 191: 2459572

Antenna 191: 2459571

Antenna 191: 2459570

Antenna 191: 2459569

Antenna 191: 2459566

Antenna 191: 2459565

Antenna 191: 2459564

Antenna 191: 2459563

Antenna 191: 2459562

Antenna 191: 2459561

Antenna 191: 2459560

Antenna 191: 2459559

Antenna 191: 2459558

Antenna 191: 2459556

Antenna 191: 2459552

Antenna 191: 2459551

Antenna 191: 2459549

Antenna 191: 2459542

Antenna 191: 2459541

Antenna 191: 2459540

Antenna 191: 2459536

Antenna 191: 2459535

Antenna 191: 2459534

Antenna 191: 2459533

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶